Dynamic optimization of a batch transesterification process for biodiesel production

De, Riju; Bhartiya, Sharad; Shastri, Yogendra

doi:10.1109/indiancc.2016.7441115

Cited by 3 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model-based control approaches are popularly being used for batch process control [1,2,3,4,5]. Model predictive control is an advanced control strategy extensively used in the process industries [6,7] and employs mathematical programming to solve a constrained, possibly non-convex, optimisation problem. The key issue here is that online computation required at each time step to obtain the optimal control input profile is very high.…”

Section: Introductionmentioning

confidence: 99%

TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control

Joshi¹,

Kodamanaa²,

Kandath³

et al. 2022

Preprint

View full text Add to dashboard Cite

Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting plant-model mismatch, these problems become harder to address for advanced model-based control strategies. Reinforcement Learning (RL), wherein an agent learns the policy by directly interacting with the environment, offers a potential alternative in this context. RL frameworks with actor-critic architecture have recently become popular for controlling systems where state and action spaces are continuous. It has been shown that an ensemble of actor and critic networks further helps the agent learn better policies due to the enhanced exploration due to simultaneous policy learning. To this end, the current study proposes a stochastic actor-critic RL algorithm, termed Twin Actor Soft Actor-Critic (TASAC), by incorporating an ensemble of actors for learning, in a maximum entropy framework, for batch process control.

show abstract